import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTMSNForImageClassification, ViTMSNModel
				from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     Dict     ,  snake_case__     :     int=1_3     ,  snake_case__     :     Tuple=3_0     ,  snake_case__     :     Optional[int]=2     ,  snake_case__     :     List[str]=3     ,  snake_case__     :     int=True     ,  snake_case__     :     Optional[int]=True     ,  snake_case__     :     Union[str, Any]=3_2     ,  snake_case__     :     Union[str, Any]=5     ,  snake_case__     :     Dict=4     ,  snake_case__     :     Optional[int]=3_7     ,  snake_case__     :     Optional[Any]="gelu"     ,  snake_case__     :     Any=0.1     ,  snake_case__     :     int=0.1     ,  snake_case__     :     Dict=1_0     ,  snake_case__     :     List[Any]=0.02     ,  snake_case__     :     Dict=None     ,  )      ->	Any:
				_lowerCamelCase				   =  parent
				_lowerCamelCase				   =  batch_size
				_lowerCamelCase				   =  image_size
				_lowerCamelCase				   =  patch_size
				_lowerCamelCase				   =  num_channels
				_lowerCamelCase				   =  is_training
				_lowerCamelCase				   =  use_labels
				_lowerCamelCase				   =  hidden_size
				_lowerCamelCase				   =  num_hidden_layers
				_lowerCamelCase				   =  num_attention_heads
				_lowerCamelCase				   =  intermediate_size
				_lowerCamelCase				   =  hidden_act
				_lowerCamelCase				   =  hidden_dropout_prob
				_lowerCamelCase				   =  attention_probs_dropout_prob
				_lowerCamelCase				   =  type_sequence_label_size
				_lowerCamelCase				   =  initializer_range
				_lowerCamelCase				   =  scope

				# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				_lowerCamelCase				   =  (image_size // patch_size) ** 2
				_lowerCamelCase				   =  num_patches + 1
		def   _snake_case       (       self     :     Dict		)      ->	Any:
				_lowerCamelCase				   =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				_lowerCamelCase				   =  None
				if self.use_labels:
						_lowerCamelCase				   =  ids_tensor([self.batch_size]     ,  self.type_sequence_label_size		)

				_lowerCamelCase				   =  self.get_config()

				return config, pixel_values, labels
		def   _snake_case       (       self     :     str		)      ->	Tuple:
				return ViTMSNConfig(
				    image_size=self.image_size     ,  patch_size=self.patch_size     ,  num_channels=self.num_channels     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  initializer_range=self.initializer_range     ,  )
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     str     ,  snake_case__     :     Dict     ,  snake_case__     :     int		)      ->	str:
				_lowerCamelCase				   =  ViTMSNModel(config=snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__		)
				self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)		)
		def   _snake_case       (       self     :     int     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     int		)      ->	Dict:
				_lowerCamelCase				   =  self.type_sequence_label_size
				_lowerCamelCase				   =  ViTMSNForImageClassification(snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__     ,  labels=snake_case__		)
				print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'		)
				print('Labels: {labels}'		)
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)		)

				# test greyscale images
				_lowerCamelCase				   =  1
				_lowerCamelCase				   =  ViTMSNForImageClassification(snake_case__		)
				model.to(snake_case__		)
				model.eval()

				_lowerCamelCase				   =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
				_lowerCamelCase				   =  model(snake_case__		)
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)		)



		def   _snake_case       (       self     :     List[str]		)      ->	Optional[Any]:
				_lowerCamelCase				   =  self.prepare_config_and_inputs()
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  config_and_inputs
				_lowerCamelCase				   =  {'pixel_values': pixel_values}
				return config, inputs_dict





@require_torch
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
		lowerCAmelCase_     =   (
		    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		def   _snake_case       (       self     :     str		)      ->	Optional[int]:
				_lowerCamelCase				   =  ViTMSNModelTester(self		)
				_lowerCamelCase				   =  ConfigTester(self     ,  config_class=snake_case__     ,  has_text_modality=snake_case__     ,  hidden_size=3_7		)
		def   _snake_case       (       self     :     List[Any]		)      ->	Union[str, Any]:
				self.config_tester.run_common_tests()
		@unittest.skip(reason='ViTMSN does not use inputs_embeds'		)
		def   _snake_case       (       self     :     Any		)      ->	Any:
				pass
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  model_class(snake_case__		)
						self.assertIsInstance(model.get_input_embeddings()     ,  (nn.Module)		)
						_lowerCamelCase				   =  model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(snake_case__     ,  nn.Linear		)		)
		def   _snake_case       (       self     :     Tuple		)      ->	Dict:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  model_class(snake_case__		)
						_lowerCamelCase				   =  inspect.signature(model.forward		)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_lowerCamelCase				   =  [*signature.parameters.keys()]

						_lowerCamelCase				   =  ['pixel_values']
						self.assertListEqual(arg_names[:1]     ,  snake_case__		)
		def   _snake_case       (       self     :     int		)      ->	Tuple:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*snake_case__		)
		def   _snake_case       (       self     :     str		)      ->	Any:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*snake_case__		)



		@slow
		def   _snake_case       (       self     :     int		)      ->	Tuple:
				for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_lowerCamelCase				   =  ViTMSNModel.from_pretrained(snake_case__		)
						self.assertIsNotNone(snake_case__		)



def 	lowerCamelCase					(	)   ->    Optional[Any]:
		_lowerCamelCase				   =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
		return image





@require_torch
@require_vision
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		@cached_property
		def   _snake_case       (       self     :     Tuple		)      ->	Dict:
				return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'		) if is_vision_available() else None



		@slow
		def   _snake_case       (       self     :     Dict		)      ->	int:
				torch.manual_seed(2		)
				_lowerCamelCase				   =  ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'		).to(snake_case__		)

				_lowerCamelCase				   =  self.default_image_processor
				_lowerCamelCase				   =  prepare_img()
				_lowerCamelCase				   =  image_processor(images=snake_case__     ,  return_tensors='pt'		).to(snake_case__		)

				# forward pass
				with torch.no_grad():
						_lowerCamelCase				   =  model(**snake_case__		)

				# verify the logits
				_lowerCamelCase				   =  torch.Size((1, 1_0_0_0)		)
				self.assertEqual(outputs.logits.shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor([-0.0803, -0.4454, -0.2375]		).to(snake_case__		)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,  snake_case__     ,  atol=1e-4		)		)
from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge





@total_ordering
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __lt__(       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				return self[-1] < other[-1]



		def __eq__(       self     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	Union[str, Any]:
				return self[-1] == other[-1]



def 	lowerCamelCase					(	UpperCamelCase     :				list							)   ->    list:
		_lowerCamelCase				   =  []
		# sort into stacks
		for element in collection:
				_lowerCamelCase				   =  Stack([element]							)
				_lowerCamelCase				   =  bisect_left(UpperCamelCase			,       UpperCamelCase							)
				if i != len(UpperCamelCase							):
						stacks[i].append(UpperCamelCase							)
				else:
						stacks.append(UpperCamelCase							)

    # use a heap-based merge to merge stack efficiently
		_lowerCamelCase				   =  merge(*(reversed(UpperCamelCase							) for stack in stacks)							)
		return collection


if __name__ == "__main__":
				A						=			input('Enter numbers separated by a comma:\n').strip()
				A						=			[int(item) for item in user_input.split(',')]
				print(patience_sort(unsorted))